1. Field Of The Invention
The present invention relates to a system which converts low-resolution multi-value image data, such as gray-scale or color image data, into high-resolution binary image data.
2. Description Of The Related Art
Binary images are formed from black and white pixels only. In contrast, color and gray-scale images are formed from pixels that vary in density to produce colors in addition to black and white. In order to depict such variations, image processing systems represent color and gray-scale pixels using multi-value image data.
In general, multi-value image data can be used to represent many different pixel density values. For example, multi-value image data for pixels of a gray-scale image typically represents density values from 0 to 255, with 0 representing white, 255 representing black, and the values between 0 and 255 representing varying shades of gray. In order to represent such different pixel density values, several bits of data must be used. In the gray-scale image having 256 different levels of gray, for example, eight bits (i.e., 2.sup.8 =256) are required to represent a single pixel of the multi-value image data.
Since multi-value image data can require a large number of bits, particularly in cases where a large number of pixel density values are desired, multi-value image data can take a long time to input, and can require a great deal of storage space. In order to reduce the amount of data to be input and stored, it is common practice for image processing systems to input multi-value images at a low resolution, usually between 100 and 200 dots per inch (hereinafter "dpi").
While low-resolution multi-value image data may be sufficient for many types of processing, optical character recognition (hereinafter "OCR") processing requires higher resolution image data, generally at least 300 dpi, in order to achieve accurate results. However, due to time and storage constraints, as noted above, it is often not practical to input and store multi-value image having a high resolution.
Accordingly, there exists a need for an image processing system that can input low-resolution multi-value image data, yet still achieve accurate OCR processing results.